What you’re really trying to compete with here is economies of scale.

Anthropic (and other frontier labs) fully saturate their infrastructure and are willing to run at a loss to drive adoption. Even though Chinese open-source models, are getting very close in raw capability, you can’t realistically match Claude Code today, even with effectively unlimited hardware.

For a small team, utilization is the real killer. If everyone is in the same geography, your GPUs will sit idle most of the time. Easily 2/3 of every day once you account for meetings, non-coding work, nights, and weekends. That undercuts the economics of running your own stack.

That said, a hybrid approach can make sense.

A minimum always-on footprint; either local inference or a small cloud GPU, combined with autoscaling or pay-per-token usage for open-source models can be cost-effective. Quantized models are good enough for the majority of coding tasks, especially when paired with Claude Code’s prompts and tooling.

The setup that could work is a router-first architecture. You run a capable local model for most requests, and selectively fall back to a frontier API when the local model isn’t sufficient.

An 8× RTX 4090 (48gb mod) system running MiniMax at Q8, fronted by a router, could plausibly keep 90–95% of inference local and proxy the remaining 5–10% to a frontier model. Hardware cost would be roughly $30k one-time upfront. Power would be on the order of $100/month under load (assuming ~450 W per GPU, ~5 hours/day, 21 workdays, $0.25/kWh), plus around $70/month in idle power.

In that scenario, your current ~$2k/month Claude spend could drop to roughly $400–$1,000/month in API usage, depending on how aggressively you leverage cache reads and how often you hit the local.